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Although the COVID-19 pandemic drew special attention to the importance of biomedical engineering, universities like Cornell have been investing in the subject for decades. That investment will substantially increase with the creation of new biomedical engineering facilities in Thurston Hall on Cornells Ithaca campus.

Cornell University is ranked as the ninth best engineering school in the country and has a 45-year long history of biomedical engineering research. Despite this history, the Department of Biomedical Engineering was only formally established in 2004, and in 2015 the Meinig School of Biomedical Engineering was created, making Biomedical Engineering a relatively new major in the College of Engineering.

Now nearly two decades old, the Biomedical Engineering Department still does not have its own building on campus. This is set to change in 2024 with the $40 million expansion and renovation of Thurston Hall which, 20 years after the establishment of the Department of Biomedical Engineering, will become the new home of the Meinig School and other departments such as Material Sciences and Engineering.

Professor Chris Schaffer, Meinig School of Biomedical Engineering, said that a key reason for the expansion was the departments current lack of teaching space.

When we first started out, we were shoving lab-based educational activities into small spaces in the basement of Weill Hall, which is where much of our research infrastructure is located, Schaffer said. But as the major has grown, we have completely outgrown these spaces, and now a lot of our lab-based teaching is scattered all over the place.

The majors growth has been continuous: The Class of 2024 is the largest class in the departments history.

The research spaces in Weill Hall worked fine for teaching lab activity when the major was 20 or 30 people, but now my [biomedical circuits signals and systems] class has 60 people alone, Schaffer said. With this kind of dramatic growth, having space that is purpose-built and architecturally designed for the purpose of lab-based teaching will make it easier for us to teach and it will make it a better environment for students to learn in.

BME students appear to concur that the current shared teaching and research space was insufficient.

There are only so many labs that can be used in Weill Hall for teaching undergrads as well as the research itself, said Parker Dean 22 M.S. 23. Weill Hall itself is a building shared with other departments, and the new undergraduate BME program seems to have put everything under stress.

Prof. Marjolein van der Meulen, the James M. and Marsha McCormick Director of Biomedical Engineering and Swanson Professor of Biomedical Engineering, has used her administrative position to promote the departments need for new teaching space, which would allow the laboratories in the basement of Weill Hall to be used exclusively for research.

Lab space is the most expensive space to build, van der Meulen said. You do not need as expensive facilities to renovate for teaching, and Weill already has the necessary and expensive elements of lab space. So, if we move teaching operations out of Weill, that space now becomes available for our research.

Programs outside biomedical engineering have also received space in the new building. Prof. Newton de Faria, Director of the Masters of Engineering Program, has been heavily involved with expanding the Masters of Engineering space in the building, which will be located on the first floor. The space will be a combination of a studio, design laboratory and fabrication spaces.

The objective [of this space] was to emulate the industry, de Faria said. This provides students with the opportunity to have the equivalent spaces of the industry while they pursue their education.

The addition and renovation of Thurston Hall will also provide faculty and students with a home on the engineering quad. According to Schaffer, the lack of space on that quad for biomedical engineering undergraduates has isolated them from their College of Engineering peers.

Good engineering is done when engineers with different areas of expertise work together and collaborate to solve a problem, Schaffer said. I think this will become far more natural when our students spend much more of their time in the engineering quad instead of running all over the place.

For van der Meulen, a place on the engineering quad is also a matter of identity, helping biomedical engineering students feel part of engineering society and culture.

Within engineering, identity is important. This addition adds a new community space to Thurston where engineering students can congregate and socialize, and will give them a home that is close to their project teams in Upson, the Duffield Atrium, Mattins caf and more, van der Meulen said. The new expansion will put a new face on the quad.

Continued here:

Thurston Hall to Expand, House Biomedical Engineering - Cornell University The Cornell Daily Sun

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As large amounts of data, from both external and internal data sources, have become central to running an organization, a pipeline of technical staffing roles has been developed to manage the collection and processing of that data.

Down in the engine room, if you will, is a data engineer who integrates multiple sources of data and manages the operations that make and keep the data available for business analysis.

On the top deck is the data analyst, who serves the data from largely pre-formed models to nontechnical business users so they can perform their work.

Mid-deck, between these two, is the data analytics engineer. This is a specialist who understands both data engineering technology and the data analysis needs of a business, and thus can build the analytical models that the upper-deck data analysts and business end users need to fulfill their roles.

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Therefore, a data analytics engineer is a person who combines the skills of the data analyst and software engineer to source and transform data for easy analysis. Because of their technical dexterity and business acumen, they have become quite valuable as members of the data team. This article details the duties and requisite skills of the analytics engineer, as well as the remuneration prospects of the role.

The analytics engineer is a member of a data team who is responsible for efficient, integrated data models and products. They build useful, well-tested and documented dataset representations and tools that the rest of the company can use to answer their questions.

They move and transform data from the source so that it can be easily analyzed, visualized and worked upon by the data analyst or business user. Not only that, but they have the technical skills to apply software engineering best practices such as Version Control and CI/CD, but also need to communicate effectively with stakeholders about the use of these tools.

The datasets created by a data analytics engineer allow end-users to comprehend and examine the information within the data. An analytics engineer combines business strategy and technical data knowledge to translate complex information and illustrate them clearly as visual representations known as data models. They collaborate with data analysts and data engineers to provide simple visual representations of data patterns and communicate their meaning to coworkers, stakeholders and end-users.

The transition to cloud data warehouses, evolution of self-service business intelligence (BI) tools and introduction of data ingestion tools have contributed to significant shifts in data tooling. Roles and responsibilities within traditional data teams are changing.

With the shift to an extract, load, transform (ELT) procedure, data now drops in the warehouse before it has been transformed. This creates an opportunity for skilled technical analysts who are both well-versed with the business and the technical skills required to model the raw data into neat, well-defined datasets. This requires the skills of both a software engineer and a data analyst, which the analytics engineer possesses.

Analytics engineers handle the data itself, as well as managing and sorting data. It is their job to make sure data is ingested, transformed, scheduled and ready to be used for analytics by all who may require it. Many analytics engineers are the orchestrators of the modern data stack, and they decide on and apply tools for ETL/ELT.

The analytics engineer is responsible for implementing and managing a data warehouse to ingest data. They also decide on the best tools to ingest data from different sources into this warehouse. Then they model the data to be used by analysts and schedule tests to simplify these models. The basic duties of the analytics engineer include:

Engineers are responsible for ingesting data into the warehouse and making sure that datasets are maintained. They are the first to be notified of any issue in the pipeline, so they can fix it.

This is the process of building visual representations of data and relating connections between different information locations and systems. Analytics engineers are charged with modeling raw data into datasets that enable analytics across the company. These datasets act as a central source of truth, making it easier for business analysts and other stakeholders to view and understand data in a database.

The engineer creates data pipelines and workflows to move data from one point to another, and coordinates the combining, verifying and storing of that data for analysis. The engineer understands everything about data orchestration and automation.

They enable other team members like data analysts and data scientists to be more effective. Whether by sharing tips for writing better SQL, reworking a dataset to contain a new metric or dimension, or training them on how to apply best practices for software engineering. This approach is called dataops (a methodology that integrates data engineering, data analytics and devops). A few best practices that can be optimized include version control, data unit testing as well as continuous integration and continuous delivery (CI/CD).

As a member of a team, they collaborate with team members to collect business requirements, define successful analytics outcomes and design data models.

Depending on the company and role specifications, a data analytic engineer may be required to perform some or all of the following:

The analytics engineer collects information, designs data models, writes code, maintains data documentation, collaborates with data team members and communicates results to concerned stakeholders. Therefore, the Analytics Engineer blends business acumen with technical expertise and alternates between business strategy and data development.

Every company or employer looks out for a specific set of skills that they require in an analytics engineer, but some general skills and competencies are vital for every analytics engineer. These skills are discussed subsequently.

Analytic engineers typically use SQL to write transformations within data models. SQL is one of the most important skills that you need to master to become an analytics engineer, since the major portion of the analytics engineers duties is creating logic for data transformations, writing queries and building data models.

SQL is closely related to Dbt in the language it utilizes, so knowledge of the former is required for the latter. Dbt is the leading data transformation tool in the industry, which is why it is most likely that the majority of analytics engineers use this to write their data models.

Knowledge of advanced languages like R and Python is crucial for analytics engineers to handle various data orchestration tasks. Many data pipeline tools utilize Python, and knowing how to code in it is extremely useful for writing your own pipeline as an engineer.

An analytics engineer needs to be conversant with the most popular tools in a modern data stack. This means possessing experience with ingestion, transformation, warehousing and deployment tools: if not comprehensive knowledge of them, then at least the basic concepts behind each of them. Learning one tool in each part of the stack may facilitate inferential understanding of the others.

An engineer needs to have experience with tools for building data pipelines. Some of these tools include data warehouses like Snowflake, Amazon Redshift and Google BigQuery; ETL tools like AWS Glue, Talend, or others as well as business intelligence tools like Tableau, Looker, etc.

Communication is key for analytics engineers because it is their responsibility to ensure that everyone is updated on the status of data. They need to communicate with relevant individuals when data quality is compromised or when a pipeline is damaged, to understand what the business needs. They also need to collaborate with business teams and data analysts to understand what the business needs. If this isnt done, erroneous assumptions can be made on defective data, and valuable ideas and opportunities will go unnoticed. It is imperative for an analytics engineer to develop and sustain multi-functional interactions with various teams across the business.

In sum, an analytics engineer must have a robust combination of technical dexterity and stakeholder management skills to succeed.

Analytics engineers in all industries and environments now have great prospects with good remuneration scales. According to Glassdoor, the average base salary is $91,188 and $111,038 in total annually in the U.S.

The analytics engineer is tasked with modeling data to provide neat and accurate datasets so that different users within and outside the company can understand and utilize them. The role involves gathering, transforming, testing and documenting data. It requires key skills in terms of communication, software engineering and programming.

The role of the analytics engineer is fairly new to the data analytics niche, but it is fast gaining traction and recognition as more and more people realize its worth.

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Data analytics engineer: Defining the role and skill requirements - VentureBeat

Dr. Bing Yu, associate professor in the Marquette University and Medical College of Wisconsin Joint Department of Biomedical Engineering, has received a $1.54 million R01 research grant from the National Institutes of Health to develop an imaging tool that would more accurately detect positive tumor margins during breast conserving surgeries.

Yus team, composed of colleagues from the Marquette Opus College of Engineering and MCW, aims to develop and implement an imaging tool for use in surgical settings. The goal is to significantly reduce the unnecessary additional surgery for women who undergo breast conserving surgery.

Women with positive margins after breast-conserving surgery have a twofold increase risk of cancer recurrence and are recommended to undergo additional re-excision surgery to achieve negative margins, Yu said. The risk of these additional surgeries can lead to significant emotional, cosmetic and financial burdens for patients and their caregivers. There have been new technologies proposed in the area, but none has demonstrated the capability of analyzing an entire lumpectomy specimen with both adequate resolution and time efficiency in a clinical setting.

Researchers will develop a microscope that utilizes deep ultraviolet scanning and deep learning to provide subcellular resolution and a rapid examination of freshly excised tumor specimens during breast conserving surgery. They propose that there are significant subcellular optical contrasts that can be identified by these methods to differentiate breast cancer cells from normal tissue. The result would be a platform technology that can be used with other imaging modalities or adapted for detection of other cancer or noncancer conditions.

This is a tremendous opportunity from the National Institutes of Health, and I am thrilled for Dr. Yu and his team from Marquette and the Medical College of Wisconsin, said Dr. Kristina Ropella, Opus Dean in the Opus College of Engineering. An imaging device of this nature will improve the patient experience by reducing additional surgeries during an already stressful time. Its impact will be measured not just in the limiting of medical risk, but also the mental and financial well-being of patients and their loved ones.

Dr. Taly Gilat-Schmidt, professor of biomedical engineering at Marquette, is a co-investigator on the project, while Dr. Dong Hye Ye, former assistant professor of electrical and computer engineering at Marquette and current assistant professor of computer science at Georgia State University, is a multiple principal investigator on the project. Dr. Tina Yen, professor of surgery, and Dr. Julie Jorns, associate professor of pathology, are co-investigators from Medical College of Wisconsin, with Yen serving as MCWs principal investigator.

The National Institutes of Healths Research Project Grant (R01) is the original and historically oldest grant mechanism used by the NIH. The R01 provides support for health-related research and development based on the mission of the NIH. R01s can be investigator-initiated or can be solicited.

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Engineering professor receives $1.54 million NIH grant to detect tumor margins more accurately in breast conserving surgery - Marquette Today -...